Grounding practices within the materiality of geography is an important technique for studying the complexity of digital phenomena.• The DIGO (Discourses, Infrastructures, Groupings, and Outcomes) framework uses these categories to guide data selection for locating digital phenomenon in material geographies. • This article applies the DIGO framework to blockchain (using data about tweets, miners, firms, and ICOs) to show how this digital practice connects to and across material geographies.Digital phenomena pose unique challenges to social science researchers investigating the impact of new and changing technologies. In part, this challenge derives from the constantly evolving practices, actants, and geographies enrolled in the digital. When these phenomena are coupled with over-the-top expectations and media hype, initial impressions often mask the complicated and nuanced ways new technologies are put to use. Blockchain (and its original application Bitcoin) represent one of these new, unstable digital phenomena that simultaneously captures public imagination and generates powerful discourses of disruption and change. One way of clarifying the messiness of technologies like blockchain is to ground its practices within the materiality of geography. The DIGO framework proposed in this article uses four broad categories-discourses (measured via Twitter), infrastructures (indicated by Bitcoin mining), groupings (based on firms and exchanges), and outcomes (measured by initial coin offerings)-located in geographic space. Each category is meant to provide insight on blockchain as it unfolds across space and scale. The same framework can guide research on other digital phenomena, based on appropriate measures for each of the four DIGO foci.

Alternative credit scores have become an increasingly important tool for lenders to assess risk and authorize investment in consumer debt. Using alternative data and processing techniques that leverage machine learning (ML) and Artificial Intelligence (AI), these models are designed to bypass existing barriers to risk-based pricing, which is the idea that financial institutions offer different interest rates to consumers based on their likelihood of default. Through an algorithmic audit of one lender's (Upstart) credit scoring model, I find that alternative data, particularly whether an applicant has a bachelor's degree, strongly impacted loan outcomes. This raises important equity concerns about overhauling lending criteria via opaque models that restructure the logic of risk assessment. In following the logic of risk assessment generated by Upstart's model, I also audit three fintech-bank partnerships and examine the balance sheets of banks providing capital via Upstart's platform. This is done to demonstrate rising capital allocation to these types of loans at banks engaged in fintech-bank partnerships, in one case rising from 0.14% to 15.6% of the banks’ balance sheet over three years. My analysis shows that alternative credit scoring systems function as a key piece of calculative infrastructure, which allows some institutions to bypass barriers to risk-based pricing, and becomes an infrastructural site for tech startups to partner with financial institutions seeking out new sources of revenue.

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